What makes a good data scientist? Most employers and recruiters prioritize skills testing when searching for the perfect candidate. After all, hiring someone who lacks technical skills can be a costly mistake. However, successful data scientists also have qualities that a skill test alone cannot identify. They have a range of skills and qualities that you can’t learn from a book. So, what are they and how do you identify them?

Such is the pressure to make the right hire that firms and recruiters are increasingly turning to artificial intelligence (AI) and machine learning (ML) based solutions. According to The Guardian, an ML-powered app Headstart is being used by a number of top companies including BP, Expedia and Vodafone to help them find the best candidates. Using a series of predictive and contextual algorithms, Headstart screens candidates and matches them with suitable roles. In this post, we will look at a range of skills that all successful data scientists have. Here is our list of the five essential qualities to look for when making your next hire.

1. Data Intuition

Just because a candidate has a data science degree or a data science certification doesn’t mean they have good data intuition. Data scientists with this quality are excellent at identifying patterns within sets of structured and unstructured data. The role of data scientists is constantly evolving and they must now understand the needs of customers as well as the needs of their organization. A good interview question that will help you uncover a candidate’s ability to identify data patterns is to ask them to create a quick data visualization. Let them choose whichever programming language they feel comfortable with such as Python or R, and ask them to demonstrate their ability to pick out a key pattern in a small dataset.

2. Iterative Design

Data scientists need to be able to work as part of a much larger team in order to deliver results. In the world of big data, it’s the data scientists who ask the questions and the data analysts who provide answers. Data scientists then take these results and draw conclusions or insights before deciding upon the next step. This iterative design process is crucial to the success of any IT department, yet not all candidates will have the ability to work in this way. While it’s essential to choose a candidate with a data science certification from a reputable data science course in India or elsewhere, you also need someone who enjoys the iterative development process. During the phone screening interview or the technical interview, ask candidates to explain the last project they worked on in detail. How did they address obstacles along the way? How did they work to make improvements? The answers to these questions will help reveal whether they are able to improve products through the process of iterative design.

3. Statistical Thinking

A skills-based technical interview will tell you whether a candidate has a solid background in data science and big data analytics and should indicate whether they are good at statistical thinking. However, it’s up to the recruiter to check this during the interview stage. While a candidate’s resume may tell you that they have completed a data science course in Hyderabad or Bangalore, it may not give you a good idea of their communication skills. During the interview, ask your candidates how they would resolve a question using statistics. For instance, does the description to every YouTube video contain the word ‘and’? How would they test that? What script would they create? This question will help highlight any candidates statistical thinking ability.

4. Hacker’s Spirit

The latest research shows that over 90 percent of data scientists have a Master’s Degree so you can be fairly confident that any candidate who makes it through the screening phase and into the technical interview has a baseline competency in common programming languages such as Python and R. However, while a skills-based assessment will show you candidate’s proficiency in bash/command line, SQL and Java but it won’t tell you how they react to working with new or unfamiliar coding languages. This is known as a ‘hacker’s spirit’: can someone work with unfamiliar codes or formats or even create their own tools when they can’t find a solution?

The best data scientists have this ‘hacker’s spirit’ and a life-long love of learning. They constantly re-train and learn new coding skills on the job. A good interview task to determine whether a candidate has the willingness to learn new skills is to challenge them to explain or write in plain English how an algorithm or query would work in a coding language that is unfamiliar to them. This task gives you an insight into their ability to think, problem-solve and react to new challenges, just as they might expect to face when they work for you. They might not arrive at the correct answer but you can tell whether they have a ‘hacker’s spirit’ and are ready to face the constantly evolving challenges in your workplace.

5. Creativity

Creativity is an essential quality for any data science candidate. They may have completed a data science and big data analytics course at a prestigious university but are they able to use their knowledge to solve real-life problems? Data scientists routinely execute database runs and queries but in order to be successful, they also need to be able to design new ways of architecting queries. After all, if their results simply answer questions that have previously been asked, what new insights will your organization gain? This is where creativity comes in; can a candidate solve a real-life issue?

The best way to determine this is to give candidates a coding challenge and ask them to speak aloud as they solve it. This will give you chance to see where they have gone wrong and you’ll be able to course-correct them in real time. This gives you an insight into the quality of their thinking and their ability to develop new solutions to existing problems. Competent data scientists constantly have to design new strategies to work with structured and unstructured data.

Conclusion

Whether you are an employer or a recruiter, these five qualities of successful data scientists will give you a head start in seeking out the best candidates. When making your next hire, make sure you look for candidates with a good blend of data intuitive, statistical thinking skills, a ‘hacker’s spirit’ and a healthy dose of creativity. Data scientists with these qualities are guaranteed to help your company thrive and prosper.